1) Field of the Invention
The present invention is directed to a method utilizing an iterative and an adaptive decision process using output decoder information for use with underwater acoustic telemetry.
2) Description of Prior Art
Traditionally, coherent techniques used in underwater acoustic telemetry employ coherent signals such as multiple phase shift keying (MPSK) and multiple quadrature amplitude modulation (MQAM) signals that are then processed by an adaptive multi-channel decision feedback equalizer (DFE).
The feedback equalizer structure is usually followed by a de-interleaver and an error correction decoder operating in a single pass fashion. The de-interleaver randomizes the errors, and the error correction decoder attempts to correct these randomly distributed errors. The error correction decoder is usually a Viterbi decoder for a convolutional code. The performance obtained using this standard algorithm is acceptable in many situations.
However, the performance can be improved by a different method. This desire for performance improvement leads to higher performance algorithms whose complexity is orders of magnitude greater than the standard decision feedback equalizer followed by de-interleaving and decoding. The turbo-equalization algorithm is one such algorithm that performs much better than the normal algorithm at a cost of high complexity.
The turbo-principle, which was first applied to concatenated codes, can be applied to many detection and decoding problems. The purpose of turbo-coding is to build a strong code by concatenation of simple component codes with a large interleaver so that the decoding can be performed with manageable complexity.
In turbo-equalization, the channel with inter-symbol interference (ISI) including the transmitter and receiver filters is regarded as a linear finite state machine—serially concatenated to the channel convolutional encoder.
In most cases, a serially-concatenated system with an interleaver consists of an outer code; an interleaver permuting the outer code words bits; and an inner finite state machine whose input words are the permuted outer codewords. There are different examples for serially-concatenated systems.
One example is the concatenation of a channel encoder and a non-linear modulator with memory (for example: a continuous phase frequency-shift keying (CPFSK) modulator). Also, concatenation of a convolutional code and a channel with memory can be considered as a serially-concatenated system, and the iterative detection algorithms can be applied to this system.
Iterative detection schemes are sub-optimum detection algorithms with limited complexity for these systems. The optimum decoding algorithms need a trellis with a huge number of states for system memories that are considered. For example, for a system with “v” memories in a convolutional encoder and with an ISI channel with “M” memories and an interleaver size “N”, the optimal decoding needs a trellis with 2(v+m+N) states.
The soft-in soft-out (SISO) algorithms can be used for both channel equalization and decoding. In such a system, a complex valued sequence “y” can be observed at the output of the receiver filter. The equalizer delivers Log values LE(c;0) about coded bits. After de-interleaving, the channel decoder delivers Log values LD(û) about information bits and Log values LD(c;0) about coded bits. The log likelihood ratio (LLR) values at the output of the decoder include an extrinsic and an intrinsic part. The extrinsic part is the incremental information about the current bit obtained through the decoding process from all the other bits in the block. It can be calculated by subtraction of the LLR values. The extrinsic information is interleaved and fed back to the equalizer where the information is used as a priori information LE(u;I) in the new decoding iteration.
However, the complexity of the turbo-equalizer is orders of magnitude greater than the DFE. Complexity grows with channel memory length, modulation level, and spatial and/or time diversity combining. Lower complexity, better performing algorithms than the standard DFE are desired which have lower complexity than that associated with turbo-equalization.